Al Akhawayn University in Ifrane - SHSS Graduate Handbook



Big Data: introduction, environmentand applications 3(3-0)

The course introduces Big Data management and techniques that can be applied to massive datasets in distributed environments. The course covers the Map-Reduce parallel computing paradigm and Hadoop distributed file system. The course reviews data storage and preparation for applications, including some machine learning algorithms used for mining knowledge in databases, including NoSQL.

Descriptive Statistics                                                                         3(3-0)

Pre-requisite: MTH 3301 (Engineering Probability)

This course covers the fundamental concepts that will provide students with the tools necessary to address routine statistical analyses that are applied in areas as diverse as human genomics and Big Data analytics. Students will be exposed to R, Bayesian networks, Expectation Maximization (EM) algorithm, principal component analysis, Regression Methods, Hypothesis testing, Parameter Estimation, t-test, confidence interval, Analysis Of Categorical Data, Bootstrapping, Cross Validation and permutation tests.


Data Mining                                                                                       3(3-0)

The course covers the most popular machine learning techniques used for “mining” knowledge that lies buried in an information system, including association rule mining, automatic cluster detection, memory based reasoning, artificial neural networks, decision trees. It shows how these techniques can be applied for making better decisions. The course discusses case studies that provide good models for such applications.


Mining of Massive Datasets                                                              3(3-0)

Pre-requisite: CSC5345 (Data Mining), CSC 3315 (Analysis of Algorithms)

This course focuses on data mining of very large amounts of data that may not fit in main memory.  Further, the course takes an algorithmic perspective. The principal topics covered are: (i) Similarity search, including the key techniques of min hashing and locality-sensitive hashing; (ii) Data-stream processing and specialized algorithms for dealing with such fast streaming data before they are lost; (iii) The technology of search engines, including Google’s PageRank, link-spam detection, and the hubs-and-authorities approach; (iv) Managing advertising and recommendation systems; (v) Algorithms for analyzing and mining the structure of very large graphs, especially social-network graphs; (vi) Techniques for obtaining the important properties of a large dataset by dimensionality reduction, including singular-value decomposition and latent semantic indexing; and (vii) Machine-learning algorithms that can be applied to very large data, such as support-vector machines.

Web and Text Mining                                                                                   3(3-0)

Much of big data are acquired from the web and social media as text. This course covers techniques and algorithms that pertain to text processing and analysis algorithms as they pertain to analytics, recommendation, and prediction. Topics include Document storage systems, web advertisement, Frameworks for the web-scale data analytics, and Frameworks for the incremental data processing.



Data Visualization                                                                             3(3-0)

This course will introduce students to the field of data visualization. Students will learn basic visualization design and evaluation principles, particularly for large datasets. Specifically, the students will also learn techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/graph-based data. Additionally, students will utilize open source such as R and/or commercial tools to prototype many of these techniques on existing datasets.

Selected Topics in Data Science and Applications                         3(3-0)

This course aims to expose students to current trends in cutting-edge research and business applications of Data Science and Business Intelligence. It emphasizes broadening students' perspective by exposing them to real applications and open problems. Topics covered will include the use of Data Science and Business Intelligence in Banking, insurance, medical, cyber security etc. delivered by speakers from Industry and research labs. 



Internet of Things and Sensor Networks                                        3(3-0)

Pre-requisite: CSC3352 (Computer Communications)

The course covers the following areas: Internet in general, its architecture, layers, protocols, packets, services, performance parameters of a packet network as well as applications such as web, Peer-to-Peer, sensor networks, and multimedia. Transport services: TCP, UDP, socket programming.  Network layer: forwarding & routing algorithms (Link, DV), IP-addresses, DNS, NAT, and routers. Local Area Networks, MAC level, link protocols such as: point-to-point protocols, Ethernet, WiFi 802.11, cellular internet access, and Machine-to-machine. Mobile Networking: roaming and handoffs, mobile IP, and ad hoc and infrastructure less networks. Smart homes, Wireless Personal area networks.  Arduino hardware platform.

Final Project                                                                                                              3(3-0)

Pre-requisite: Approval of Graduate Advisor

Students pursuing the professional program must register for and complete this course.

Final Thesis                                                                                                                6(6-0)

Pre-requisite: Approval of Graduate Advisor

Students pursuing the research program must register for and complete this 2-semester course.


Program Structure

This 30-SCH program is designed to be completed in one and a half calendar year.

The requirements of the MSDS program consist of 9 courses and a 1 semester MS Project (3 SCH) or 8 courses and an MS Thesis (6 SCH).


The courses are divided into 7 major courses and 1 (or 2) elective(s).


The undergraduate foundation courses required for the MSDS are:

  1. Database Systems,
  2. Analysis of Algorithms
  3. Engineering Probability and Statistics or Statistics for Engineers.


MSDS COURSES:                                                                                         30 SCH

Big Data: introduction, environment and  applications




Descriptive Statistics






 Data mining





Mining  of Massive Datasets




Web and Text Mining





Data Visualization





Selected Topics in Data Science and applications


Elective #1


Elective #2


Final MS project




Final MS Thesis


Students can select any of the following courses for the electives:

Business Intelligence and Data Warehousing


Decision Support Geographical Information Systems


Graph Theory and Applications



Internet of Things and Sensor Networks



Computer Project Management


Cloud Computing


Artificial Intelligence


Knowledge-based systems


Natural language Processing


Advanced Statistics




 In order to earn an MSDS degree, a student must:


  1. Fulfill the major (21 SCH) and electives (3 or 6 SCH) course requirements for the MSDS, in addition to any undergraduate pre-requisite courses;
  2. Complete and defend the MS Project (CSC 5343: 3 SCH) or the MS thesis (CSC 5333: 6 

SCH) successfully;

3.  Have a CGPA of at least 3.00;

4.  Earn a grade of B- or better in all courses counting towards the MSDS.

Program Intended Learning Outcomes

Students completing this program are expected to be able to:

  1. Setup Big Data processing architectures and environments;
  2. Apply a variety of Big Data analytics techniques (including data mining and statistical  techniques) for prediction and recommendation;
  3. Deal with a variety of Big Data sources including transactional, web, text, social media and stream sensory sources.


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